Face recognition (FR) has been a highly active research area for many years. A typical approach involves two tasks: feature extraction and classification. Commonly-used feature extraction methods include subspace techniques such as principle component analysis (PCA or eigenface), independent component analysis (ICA), linear discriminant analysis (LDA or fisherface), and so on [1, 2]. With features extracted, classifiers based on techniques such as nearest neighbor and/or support vector machines can then be used to perform recognition. The above feature extraction methods are well-understood and in a sense have reached their maturity. Researchers are now looking for different methods and theories to address the persisting challenges in FR, such as expression, illumination and pose variation, dimensionality reduction, and/or the like. In addition, reducing the space complexity and, in particular, the operational dimensionality of the classifier may be important for practical applications involving large databases.
A need, therefore, exists for FR techniques that address these and other issues.